code: def test_1m_1cf_wgt_metric_dict():
      target = MetricFrame(
          metrics={"recall": skm.recall_score},
          y_true=y_t,
          y_pred=y_p,
          sensitive_features=g_2,  # Unused
          control_features=g_1,
          sample_params={"recall": {"sample_weight": s_w}},
      )
      assert target._user_supplied_callable is False
  
      assert isinstance(target.overall, pd.DataFrame)
      assert target.overall.shape == (2, 1)
      assert np.array_equal(
          target.overall.index.names, ["control_feature_0"]
      )
      mask_a = g_1 == "aa"
      mask_b = g_1 == "ba"
      exp_recall_a = skm.recall_score(
          y_t[mask_a], y_p[mask_a], sample_weight=s_w[mask_a]
      )
      exp_recall_b = skm.recall_score(
          y_t[mask_b], y_p[mask_b], sample_weight=s_w[mask_b]
      )
      assert target.overall["recall"]["aa"] == exp_recall_a
      assert target.overall["recall"]["ba"] == exp_recall_b
  
file_path: fairlearn/test/unit/metrics/test_metricframe_overall.py
imports: import numpy
  import pandas
  import pytest
  import sklearn.metrics
  from fairlearn.metrics import MetricFrame
  from .data_for_test import g_1, g_2, g_3, s_w, y_p, y_t
label_desc: Use image node_id 9 for calling a global function with example usage: test_1m_1cf_wgt_metric_dict() without return types